首页|Findings from National Institute of Technology Raipur in the Area of Parkinson's Disease Described (An Ensemble Technique To Predict Parkinson's Disease Using M achine Learning Algorithms)
Findings from National Institute of Technology Raipur in the Area of Parkinson's Disease Described (An Ensemble Technique To Predict Parkinson's Disease Using M achine Learning Algorithms)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Neurodegenerative Diseases and Co nditions-Parkinson's Disease is the subject of a report. According to news rep orting originating from Chhattisgarh, India, by NewsRx correspondents, research stated, "Parkinson's Disease (PD) is a progressive neurodegenerative disorder af fecting motor and non-motor symptoms. Its symptoms develop slowly, making early identification difficult." Our news editors obtained a quote from the research from the National Institute of Technology Raipur, "Machine learning has a significant potential to predict P arkinson's disease on features hidden in voice data. This work aimed to identify the most relevant features from a high-dimensional dataset, which helps accurat ely classify Parkinson's Disease with less computation time. Three individual da tasets with various medical features based on voice have been analyzed in this w ork. An Ensemble Feature Selection Algorithm (EFSA) technique based on filter, w rapper, and embedding algorithms that pick highly relevant features for identify ing Parkinson's Disease is proposed, and the same has been validated on three di fferent datasets based on voice. These techniques can shorten training time to i mprove model accuracy and minimize overfitting. We utilized different ML models such as K-Nearest Neighbors (KNN), Random Forest, Decision Tree, Support Vector Machine (SVM), Bagging Classifier, Multi-Layer Perceptron (MLP) Classifier, and Gradient Boosting. Each of these models was fine-tuned to ensure optimal perform ance within our specific context. Moreover, in addition to these established cla ssifiers, we proposed an ensemble classifier is found on a high optimal majority of the votes. Dataset-I achieves classification accuracy with 97.6 % , F1-score 97.9 %, precision with 98 % and recall wit h 98 %. Dataset-II achieves classification accuracy 90.2 % , F1-score 90.2 %, precision 90.2 %, and recall 90.5 % . Dataset-III achieves 83.3 % accuracy, F1-score 83.3 % , precision 83.5 % and recall 83.3 %. These results h ave been taken using 13 out of 23, 45 out of 754, and 17 out of 46 features from respective datasets."
ChhattisgarhIndiaAsiaAlgorithmsB asal Ganglia Diseases and ConditionsBrain Diseases and ConditionsCentral Ner vous System Diseases and ConditionsCyborgsEmerging TechnologiesHealth and MedicineMachine LearningMovement DisordersNervous System Diseases and Cond itionsNeurodegenerative Diseases and ConditionsParkinson's DiseaseParkinso nian DisordersNational Institute of Technology Raipur
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